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A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks

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  • Norman R. Swanson
  • Halbert White

Abstract

We take a model selection approach to the question of whether a class of adaptive prediction models (artificial neural networks) is useful for predicting future values of nine macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria, including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolling window prediction methods indicate that multivariate adaptive linear vector autoregression models often outperform a variety of (1) adaptive and nonadaptive univariate models, (2) nonadaptive multivariate models, (3) adaptive nonlinear models, and (4) professionally available survey predictions. Further, model selection based on the in-sample Schwarz information criterion apparently fails to offer a convenient shortcut to true out-of-sample performance measures. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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Bibliographic Info

Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 79 (1997)
Issue (Month): 4 (November)
Pages: 540-550

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Handle: RePEc:tpr:restat:v:79:y:1997:i:4:p:540-550

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  1. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, American Economic Association, vol. 81(3), pages 580-90, June.
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  10. Victor Zarnowitz & Phillip Braun, 1993. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," NBER Chapters, National Bureau of Economic Research, Inc, in: Business Cycles, Indicators and Forecasting, pages 11-94 National Bureau of Economic Research, Inc.
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